pm4py.algo.organizational_mining.resource_profiles.variants package#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

Submodules#

pm4py.algo.organizational_mining.resource_profiles.variants.log module#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.organizational_mining.resource_profiles.variants.log.Parameters(value)[source]#

Bases: Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
RESOURCE_KEY = 'pm4py:param:resource_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
pm4py.algo.organizational_mining.resource_profiles.variants.log.get_dt_from_string(dt: Union[datetime, str]) datetime[source]#

If the date is expressed as string, do the conversion to a datetime.datetime object

Parameters#

dt

Date (string or datetime.datetime)

Returns#

dt

Datetime object

pm4py.algo.organizational_mining.resource_profiles.variants.log.distinct_activities(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) int[source]#

Number of distinct activities done by a resource in a given time interval [t1, t2)

Metric RBI 1.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

Returns#

distinct_activities

Distinct activities

pm4py.algo.organizational_mining.resource_profiles.variants.log.activity_frequency(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, a: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

Fraction of completions of a given activity a, by a given resource r, during a given time slot, [t1, t2), with respect to the total number of activity completions by resource r during [t1, t2)

Metric RBI 1.3 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

a

Activity

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.activity_completions(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) int[source]#

The number of activity instances completed by a given resource during a given time slot.

Metric RBI 2.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.case_completions(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) int[source]#

The number of cases completed during a given time slot in which a given resource was involved.

Metric RBI 2.2 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.fraction_case_completions(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The fraction of cases completed during a given time slot in which a given resource was involved with respect to the total number of cases completed during the time slot.

Metric RBI 2.3 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.average_workload(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The average number of activities started by a given resource but not completed at a moment in time.

Metric RBI 2.4 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.multitasking(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The fraction of active time during which a given resource is involved in more than one activity with respect to the resource’s active time.

Metric RBI 3.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.average_duration_activity(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, a: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The average duration of instances of a given activity completed during a given time slot by a given resource.

Metric RBI 4.3 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

a

Activity

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.average_case_duration(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The average duration of cases completed during a given time slot in which a given resource was involved.

Metric RBI 4.4 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.interaction_two_resources(log: EventLog, t1: Union[datetime, str], t2: Union[datetime, str], r1: str, r2: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The number of cases completed during a given time slot in which two given resources were involved.

Metric RBI 5.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

log

Event log

t1

Left interval

t2

Right interval

r1

Resource 1

r2

Resource 2

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.log.social_position(log: EventLog, t1_0: Union[datetime, str], t2_0: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The fraction of resources involved in the same cases with a given resource during a given time slot with respect to the total number of resources active during the time slot.

Metric RBI 5.2 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1_0

Left interval

t2_0

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas module#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

class pm4py.algo.organizational_mining.resource_profiles.variants.pandas.Parameters(value)[source]#

Bases: Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
RESOURCE_KEY = 'pm4py:param:resource_key'#
CASE_ID_KEY = 'pm4py:param:case_id_key'#
pm4py.algo.organizational_mining.resource_profiles.variants.pandas.get_dt_from_string(dt: Union[datetime, str]) datetime[source]#

If the date is expressed as string, do the conversion to a datetime.datetime object

Parameters#

dt

Date (string or datetime.datetime)

Returns#

dt

Datetime object

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.distinct_activities(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) int[source]#

Number of distinct activities done by a resource in a given time interval [t1, t2)

Metric RBI 1.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

distinct_activities

Distinct activities

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.activity_frequency(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, a: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

Fraction of completions of a given activity a, by a given resource r, during a given time slot, [t1, t2), with respect to the total number of activity completions by resource r during [t1, t2)

Metric RBI 1.3 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

a

Activity

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.activity_completions(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) int[source]#

The number of activity instances completed by a given resource during a given time slot.

Metric RBI 2.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.case_completions(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) int[source]#

The number of cases completed during a given time slot in which a given resource was involved.

Metric RBI 2.2 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.fraction_case_completions(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The fraction of cases completed during a given time slot in which a given resource was involved with respect to the total number of cases completed during the time slot.

Metric RBI 2.3 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.average_workload(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The average number of activities started by a given resource but not completed at a moment in time.

Metric RBI 2.4 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.multitasking(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The fraction of active time during which a given resource is involved in more than one activity with respect to the resource’s active time.

Metric RBI 3.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.average_duration_activity(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, a: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The average duration of instances of a given activity completed during a given time slot by a given resource.

Metric RBI 4.3 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

a

Activity

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.average_case_duration(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The average duration of cases completed during a given time slot in which a given resource was involved.

Metric RBI 4.4 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.interaction_two_resources(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r1: str, r2: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The number of cases completed during a given time slot in which two given resources were involved.

Metric RBI 5.1 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r1

Resource 1

r2

Resource 2

Returns#

metric

Value of the metric

pm4py.algo.organizational_mining.resource_profiles.variants.pandas.social_position(df: DataFrame, t1: Union[datetime, str], t2: Union[datetime, str], r: str, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) float[source]#

The fraction of resources involved in the same cases with a given resource during a given time slot with respect to the total number of resources active during the time slot.

Metric RBI 5.2 in Pika, Anastasiia, et al. “Mining resource profiles from event logs.” ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.

Parameters#

df

Dataframe

t1

Left interval

t2

Right interval

r

Resource

Returns#

metric

Value of the metric